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Stabilized Benders methods for large-scale combinatorial optimization, with appllication to data privacy

Author
Baena, D; Castro, J.; Frangioni, A.
Type of activity
Report
Date
2017-10-24
Repository
http://hdl.handle.net/2117/116306 Open in new window
URL
http://www-eio.upc.edu/~jcastro/publications/reports/dr2017-03.pdf Open in new window
Abstract
The Cell Suppression Problem (CSP) is a challenging Mixed-Integer Linear Problem arising in statistical tabular data protection. Medium sized instances of CSP involve thousands of binary variables and million of continuous variables and constraints. However, CSP has the typical structure that allows application of the renowned Benders’ decomposition method: once the “complicating” binary variables are fixed, the problem decomposes into a large set of linear subproblems on the “easy” co...
Citation
Baena, D, Castro, J., Frangioni, A. "Stabilized Benders methods for large-scale combinatorial optimization, with appllication to data privacy". 2017.
Keywords
Benders’ decomposition, Mixed-Integer Linear Problems, cell suppression problem, large-scale optimization, local branching, stabilization, statistical tabular data protection
Group of research
GNOM - Mathematical Optimization Group

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